Fieldops prompt refiner
Dual-runtime Agent Skills for Claude Code, Codex GPT, CC Switch, and authorized CTF workflows.
npx -y skills add download4you/n2-fieldops --skill fieldops-prompt-refinerAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 26 days oldThe repository was created 26 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Transforms rough, ambiguous, overloaded, or reusable requests into precise, execution-ready prompts for coding agents. Use when asked to improve, rewrite, refine, tighten, structure, modularize, audit, or professionalize a prompt, AGENTS.md instruction set, task brief, system prompt, or agent workflow. Preserves intent while clarifying outcome, scope, inputs, constraints, authority, verification, and deliverables. Does not apply when merely executing an already well-specified task.
SKILL.md
1.2 KB, as published. Nobody here has run it
Prompt Refiner
- Extract outcome, audience/agent, inputs, environment, and completion condition.
- Separate hard constraints from preferences and examples.
- Ask only about ambiguities that materially change safe execution.
- Remove duplication, conflict, unenforceable claims, and low-value prose.
- Organize with
references/prompt-architecture.md. - Add proportional evidence and verification requirements.
- Preserve language, terminology, intent, and authority boundary.
- Return a copy-ready prompt plus a short design note when useful.
For large reusable profiles, prefer modular skills and references over one always-loaded prompt. Never claim a prompt can override higher-priority runtime instructions.
Gives 0 of the 12 instructions most prompt engineering skills give
Counted across 563 of the 626 authors here whose files we hold, read 2026-08-06
- ask at most three clarifying questionsin 22 of 563, across 15 files
- respond in the user input languagein 14 of 563, across 9 files
- preserve the original intentin 13 of 563, across 11 files
- Establish baseline metrics and collect representative examplesin 12 of 563, across 2 files
- Identify failure modes and prioritize high-impact fixesin 12 of 563, across 2 files
- Apply prompt and workflow improvements with measurable goalsin 12 of 563, across 2 files
- Roll back quickly if quality or safety metrics regressin 12 of 563, across 2 files
- validate changes with tests and roll out in controlled stagesin 12 of 563, across 2 files
- generate quantitative baseline performance reportsin 12 of 563, across 2 files
- create representative test scenariosin 12 of 563, across 2 files
- treat prompts as codein 12 of 563, across 5 files
- test prompts on diverse inputsin 12 of 563, across 8 files
Said here and by no other author read
- Extract outcome, audience, inputs, environment, and completion condition
- Separate hard constraints from preferences and examples
- Ask only about ambiguities affecting safe execution
- Remove duplication, conflict, and unenforceable claims
- Organize with prompt architecture references
- Add proportional evidence and verification requirements
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.